An Ontology Learning Model in Grid Information Services

A secure information service for monitoring large scale grids

Grid information service is an important part of grid systems, which influences performance of grid systems directly. Therefore, it is necessary and urgent to analyze characteristics of grid information service. We analyze performance metrics of grid information service; discuss modeling of grid information and grid information service. As an example, JFreeSim is introduced and focused on simulation of grid information service. JFreeSim realizes many entity modeling and communication mechanisms among all entities, and makes system simulation in accord with the characteristic of the grid environment. Examples indicate that JFreeSim can provide users with flexibility in configuring and satisfying requirements of different information service modeling.

Grid Computing is a concept, a network, a work in progress, part hype and part reality, and it is increasingly capturing the attention of the computing community. The advancements in wireless technologies and increased number of wireless device users supported the evolution of wireless grids. Grid information server (GIS) has to maintain the most up-to-date resource status information of all devices, so that, application can be scheduled to devices that meet its resource requirements.Each wireless device is resource constrained, and its resource status keeps on varying dynamically depending upon number of applications it is executing, amount of data it is communicating, battery level, and mobility. In order to keep up-to-date resource status, a continuous monitoring is needed. The increase in number of status delivery of such monitored observations will consume lot much of bandwidth, making the database size of grid information server to grow continuously over a period of time.To solve this problem, we consider moderate number of communications of status updates that balances both bandwidth consumption and resource status accuracy. Also, we propose three methods to represent these update messages so that bandwidth requirement and latency of communication with GIS is reduced. Normal representation, Variable bit length representation, and Relative difference representation methods are proposed and analyzed. Relative difference method is analyzed in best case as well as in worst case, and is found to be more efficient compared to other two methods in terms of memory requirements.KeywordsWireless gridresource statusresource monitoring windowrelative difference

Grid provides an infrastructure to meet the high demands for resource sharing and collaboration. Semantic Grid contributes to create shared knowledge in a virtual organization (VO) based on semantic metadata. While it achieved providing relevant resource to users, a new Grid problem appears- personalized resource provision. Semantics regards the resources themselves rather than the surrounding information, so dynamic semantic meaning and underlying purpose of user are difficult to be obtained. It is obstacle of personalized resource provision. In semiotics, pragmatics is focused on how context influence the meanings of signs, the relationship between signs and users' purposes in a communication. We propose pragmatic Grid to solve the problem. The pragmatic Grid is an extension of the semantic Grid, to concern with how the Grid information can be used effectively according to user purpose and context. In this paper, we propose pragmatic Grid architecture, pragmatic information model, architecture of interoperation for resource management, and a pragmatic-aware discovery service. Finally, a case study is given to validate the applicability of personalized resources discovery in pragmatic Grid.

Ontologies are the backbone of the Semantic Web. Current grid architectures do not consider their usage, and there are no protocols nor standards in the Grid community for dealing with them. Therefore, the provision of appropriate means for accessing, querying and using ontologies effectively is a key factor if we want to enrich the current grid with semantic technologies and to support progress towards the next generation Grid, that is, the Semantic Grid.

Grid computing is the next generation of distributed system. Its goal is to create a huge, independent and powerful virtual machine, and it has been created by gathering different machines with the aim of sharing them. Resource discovery is the most fundamental phase of resource management which has been considered in our work. Considering the advantages and disadvantages of P2P and grid and their architectural similarity; using some of P2P approaches as a solution in Grid can improve the performance of Grid. Semantic technology and fuzzy logic are the two intelligent approaches, which recently apply on P2P network and Grid environment. In P2P networks, nodes are grouping based on their interests. Moreover semantic can be very useful for grouping nodes. This paper follows the identification of resource discovery in grid environment with the help of semantic and fuzzy theory and seeking to present a resource discovery algorithm with respect to exiting obstacles. The proposed approach is assessed in simulated grid environment and the results are compared with other approaches in same conditions. The goals of our approach are to obtain precious matching by clustering nodes which have services semantically related, improving search expressiveness by considering the distances between nodes (delay) in clustering phase, and decreasing response time and processing time by considering the nodes computational capabilities and free space of each node. We evaluate the performance of our approach with some distributed semantic based grid resource discovery models. The results of the experiments show the efficiency of the proposed approach in term of scalability, precision, search expressiveness and response time.

In grid environment, resources and services distributed with dynamic and heterogeneous characteristics. Efficient service discovery is one challenging issue in grid environment. In this paper, we propose a new distributed and hierarchical mechanism for service discovery in grid environment. This approach has five layers that in previous works presented in four layers. Root layer added as coverage for service index of under layers. For improvement fault tolerant in proposed approach, after find requested service, action of service discovery not stopped and continue that find several instance of requested service. These services will be sent to cache of institution layer that, other services will be replaced immediately if necessary. Also for speed up service discovery, Nodes in one level which are the same parent send query to each other directly. Performance evaluation shows that our approach achieves a good efficiency, stability and consistency.KeywordsService discoveryGrid Computing

Monitoring of Service Level Agreements is a crucial phase of SLA management. In the most challenging case, monitoring of SLA fulfillment is required in (near) real-time and needs to combine performance data regarding multiple distributed services and resources. Currently existing Grid monitoring and information services do not provide adequate on-line monitoring capabilities to fulfill this case. We present an application of Complex Event Processing principles and technologies for on-line SLA monitoring in the Grid. The capabilities of the presented SLA monitoring framework include (1) on-demand definition of SLA metrics using a high-level query language; (2) real-time calculation of the defined SLA metrics; (3) advanced query capabilities which allow for defining high-level complex metrics derived from basic metrics. SLA monitoring of data-intensive grid jobs serves as a case study to demonstrate the capabilities of the approach.

Grid Scheduling and Information Services

Owing to the dynamical, heterogeneous, complex and distributed attributes of grid information, it is hard to make a normal and uniform description. How to construct an overlay structure to support the standardized modeling is a key research matter. In this paper, we describe an extensible framework for grid information service virtualized process. We begin the study with grid information and service virtualization. Then the grid information service virtualized process is divided into three levels: category, cluster and service, in which the construction and maintenance methods are studied. The grid information joining, leaving and updating are also analyzed. Finally, the space and time complexities are analyzed, and conclusions and future work are made.

The Workload Management System is the gLite service supporting the distributed production and analysis activities of various HEP experiments. It is responsible of dispatching computing jobs to remote computing facilities by matching job requirements and the resource status information collected from the Grid information services. Given the distributed and heterogeneous nature of the Grid, the monitoring of the job lifecycle and of the aggregate workflow patterns generated by multiple user communities, and the reliability of the service are of great importance. In this paper we deal with the problem of WMS monitoring and management. We present the architecture and implementation of the WMSMonitor, a tool for WMS monitoring and management, which has been designed to meet the needs of various WMS user categories: administrators, developers, advanced Grid users and performance testers. The tool was successfully deployed to monitor the progress of WMS job submission activities during HEP computing challenges. We also describe how, for each WMS in a cluster, WMSMonitor produces status indexes and a load metric that can be used for automated notification of critical events via Nagios, or for ranking of service instances deployed in load balancing mode.

In this paper, a workflow scheduling approach, which consists of two algorithms, is proposed. A submitted workflow is first partitioned into subgraphs on the global Grid level by the graph partitioning algorithm according to features of the workflow itself and the status of selected available resource clusters. Then, at the resource cluster level, metatasks in each subgraph are allocated to computational resources by the metatask mapping algorithm. To reduce the total makespan of a workflow, the schedule of raw input data preloading are considered by the two algorithms. This two-phase approach does not require detailed resource information or control privilege on every Grid resource for Grid schedulers at the global Grid level, so that the dependence on Grid information services is reduced and the higher priority of local resource management policies is respected.

The rapid development of information technology offers people a broad information-sharing platform. We can get relevant information through information retrieval tools. In order to get higher search efficiency, the information retrieval technologies based on semantic grid are discussed. Based on analysis of the technologies of grid, semantic grid and information retrieval, the architecture of grid information retrieval system is brought out and a common model of grid information retrieval is established. The grid platform based on Globus toolkit is built which is made as the basic development platform of the system, the semantic grid information retrieval system under that platform is realized, and the designed model and algorithm are implemented and the specific experimental tests is realized. The result shows that the system has better retrieval recall factor and pertinency factor than the conventional retrieval methods, and the feasibility of system model is verified.

Nowadays, the process of data mining is one of the most important topics in scientific and business problems. Grid Computing can be used as infrastructures to provide an effective computational support for distributed data mining applications. The paper proposed a data mining architecture from large-scale distributed and semantically heterogeneous data resources in order to efficiently support knowledge acquisition. Grid technology and semantic web technology were combined to solve the difficult problem of distributed data mining. Data mining in the Grid environment can provide highly efficient and powerful data analysis and knowledge discovery solution and semantic web can effectively solve the heterogeneity of data sources and make them available for data mining.

This paper deals with information model for power grid fault diagnosis based on Common Information Model (CIM). Fault diagnosis gains information from topology information and device parameters of primary system grid based on IEC61970 standard and device information of secondary system grid based on IEC 61850. Device information contains information of protection movement wave record and protection configuration. However, the existent CIM can't fully represent power system fault diagnosis information. Thus, it is need to extend CIM. All kinds of information are based on different model, which causes different information standard. In this paper a preliminary study on CIM extensions which is based on power system fault diagnosis, has been proposed to perfect fault information model. On the basis of different standard model, this paper has discussed the theory of coordinating IEC 61850 and IEC61970 standard, and proposed using Web Ontology Language (OWL) based on Extensible Mark-up Language (XML) to the solution of this problem. A fault Information Model is then provided to the fault diagnosis system according to Component Interface Specification (CIS) in IEC61970. After receiving fault information, the fault diagnosis system starts for real-time, on-line fault diagnosis. Fault information model has solved the problem of the deficiency and non-standard of fault diagnosis information, attaining fault diagnosis information integrity and standardization, and making the fault diagnosis result fast and accurate.